Occupancy models are widely used to describe the distribution of rare and cryptic species—those that occur on only a small portion of the landscape and cannot be detected reliably during a single survey. However, model estimates of occupancy (ψ) and detection probabilities (p) are often least accurate under these circumstances. Available sampling designs for occupancy surveys include standard design, wherein each of S sites is visited K times, and removal design, wherein S sites are visited K times each or until the species of interest is detected. We propose a new conditional design, wherein each of S sites is visited one time, and sites where the species of interest is encountered during the first survey are visited an additional (K−1) times to better estimate detection probability. We used large sample properties of maximum-likelihood estimators and Markov chain Monte Carlo (MCMC) simulations to characterise our proposed conditional design and compare it to standard and removal designs across a wide range of true occupancy and detection probabilities (ψ, p = 0.1 to 0.9 by 0.1 increments), maximum visits (K) and total sampling effort (E, the number of surveys accrued across all sites). The conditional design provided more accurate estimates (lower standard or root mean squared error) of occupancy than standard or removal designs in our calculations and simulations when species were rare (ψ ≤ 0.3) as well as more accurate estimates of detection probability over most combinations of ψ and p. These low-occupancy improvements are achieved by expending a greater proportion of effort at occupied sites, improving estimates of p and thus ψ. When species are common (ψ ≥ 0.5) the removal design generally provided the most accurate occupancy estimates, whereas the standard design performed best when ψ was intermediate and during MCMC simulations when p and K were low. We recommend the conditional design for surveys of rare species and pilot studies. For multi-species surveys that include mixtures of rare and common species, a hybrid standard-conditional design with 2–3 replicates at all sites and additional replicates at sites where rare species are detected improves occupancy estimates of rare species.
Bibliographical noteFunding Information:
Foundation Grant #00039202 (H.M.S.), Delta Waterfowl Foundation
We acknowledge the University of Minnesota, National Science Foundation Grant #00039202 (H.M.S.), Delta Waterfowl Foundation (M.K.J.), Ducks Unlimited Canada (M.D.W.) and the Minnesota Department of Natural Resources (F.I., M.R.E.) for financial support. Insightful comments from the editors and two anonymous reviewers greatly improved this manuscript.
(M.K.J.), Ducks Unlimited Canada (M.D.W.) and the Minnesota Department of Natural Resources (F.I., M.R.E.) for financial support. Insightful comments from the editors and two anonymous reviewers greatly improved this manuscript.
© 2017 The Authors. Methods in Ecology and Evolution © 2017 British Ecological Society
- cryptic species
- detection probability
- rare species
- removal method
- species occurrence
- study design